12 min read
I’ve deployed production workloads on all three major cloud platforms. Dozens of them. Across startups burning through seed funding, mid-market companies running ERP migrations, and enterprises with seven-figure monthly cloud bills. And after years of watching businesses agonize over this decision — often spending more time choosing a provider than actually building anything — I’m going to give you something the cloud vendors won’t: a straight answer.
There’s no universally “best” cloud platform. But there’s almost certainly a best one for you. And the difference between picking the right one and the wrong one isn’t theoretical. It’s tens of thousands of dollars a year in unnecessary spending, engineering hours wasted fighting the platform instead of building on it, and migration costs that’ll make your CFO lose sleep.
Here’s the honest breakdown.
Market Share: What the Numbers Actually Mean
AWS owns roughly 33% of the global cloud infrastructure market. Azure sits at about 22%. Google Cloud holds around 10%. Those numbers come from Synergy Research Group, and they’ve been remarkably consistent in their trajectory — AWS slowly losing share, Azure steadily gaining, Google Cloud growing fast but from a much smaller base.
But market share doesn’t tell the story most people think it does. AWS’s dominance isn’t because it’s the best at everything. It’s because it was first. Amazon launched EC2 in 2006, and by the time Microsoft and Google showed up with serious offerings, AWS had a five-year head start in services, documentation, community knowledge, and hiring pipeline. Inertia is a powerful force. Most companies running on AWS today chose it because it was the default, not because they ran a rigorous comparison.
That doesn’t make it the wrong choice. It just means market share is a lagging indicator, not a buying signal.
The Pricing Reality Check
Here’s where things get uncomfortable for all three vendors. Cloud pricing is deliberately complex. Not accidentally. Deliberately. The opacity benefits the providers because confused buyers overspend. Period.
Let me cut through it with real numbers as of mid-2024.
Compute: Virtual Machines
For a general-purpose VM with 4 vCPUs and 16 GB RAM — the workhorse config for most web applications:
- AWS (m6i.xlarge): $0.192/hour on-demand, ~$138/month
- Azure (D4s v5): $0.192/hour on-demand, ~$140/month
- Google Cloud (e2-standard-4): $0.134/hour on-demand, ~$97/month
Google Cloud is meaningfully cheaper on compute. Not a rounding error — roughly 30% less for equivalent specs. And their sustained use discounts kick in automatically. No commitment required. You just run a VM for most of the month and the price drops. AWS and Azure require you to pre-commit with Reserved Instances or Savings Plans to get comparable rates.
With a 1-year commitment, the gap narrows:
- AWS (1-year reserved, no upfront): ~$100/month
- Azure (1-year reserved): ~$99/month
- Google Cloud (1-year committed use): ~$85/month
Google still wins. But the margins get thinner when you start layering on networking, storage, and the services that actually make a cloud platform useful.
Storage: Object Storage
For standard object storage (S3, Blob Storage, Cloud Storage) — the first 50 TB tier:
- AWS S3 Standard: $0.023/GB/month
- Azure Blob Storage (Hot): $0.018/GB/month
- Google Cloud Storage (Standard): $0.020/GB/month
Azure takes the edge on raw storage pricing. For a company storing 10 TB, that’s $180/month on Azure vs. $230/month on AWS. Not transformative, but $600/year adds up.
The Cost Nobody Talks About: Egress
This is where I get genuinely angry on behalf of my clients. Egress — the cost of moving data out of a cloud provider — is the hidden tax that turns reasonable cloud bills into outrageous ones.
- AWS: $0.09/GB for the first 10 TB/month out to the internet
- Azure: $0.087/GB for the first 5 GB free, then $0.087/GB
- Google Cloud: $0.12/GB for the first 1 TB, dropping to $0.08/GB for 1-10 TB
All three charge you to leave. And the pricing is, frankly, exploitative relative to actual bandwidth costs. Moving 1 TB of data out of AWS costs $92. Moving it between AWS regions? Also not free — $0.02/GB. That’s $20 per terabyte just to replicate your own data within the same provider’s network.
Google Cloud recently made the first 200 GB of egress free and has been more aggressive on egress pricing reductions. But none of the three deserve praise here. Egress fees are the cloud industry’s original sin, and they exist primarily to make leaving expensive. That’s the reality.
Real Scenario: A Mid-Size Web Application
Let me model something concrete. A SaaS application with 3 app servers, a managed database, 500 GB storage, 2 TB monthly egress, and a load balancer:
| Component | AWS | Azure | Google Cloud | |———–|—–|——-|————–| | 3x Compute (4 vCPU, 16 GB) | $414/mo | $420/mo | $291/mo | | Managed DB (PostgreSQL, 4 vCPU) | $350/mo | $330/mo | $310/mo | | 500 GB Object Storage | $11.50/mo | $9/mo | $10/mo | | 2 TB Egress | $184/mo | $174/mo | $160/mo | | Load Balancer | $22/mo | $20/mo | $25/mo | | Monthly Total | ~$982 | ~$953 | ~$796 |
Google Cloud saves you roughly $2,200/year over AWS for this workload. That’s not nothing — especially for a startup or small business watching every dollar. If you’re just getting started with cloud migration fundamentals, these cost differences compound fast over a multi-year horizon.
But pricing is only one axis. And honestly, it isn’t even the most important one.
Strengths by Use Case: Where Each Platform Actually Wins
AWS: The Everything Store
AWS has more services than anyone else. Over 200. Many of them are excellent. Some of them are mediocre products that exist purely to check a box on an RFP. The breadth is simultaneously AWS’s greatest strength and its most disorienting quality — finding the right service among dozens of overlapping options requires either deep expertise or expensive consultants.
Where AWS genuinely excels:
- Mature, battle-tested infrastructure. EC2, S3, RDS, Lambda — these aren’t just products, they’re industry standards. The documentation is deep. The community answers on Stack Overflow are plentiful. The hiring pool of AWS-certified engineers is the largest in the industry.
- Enterprise workloads. AWS’s compliance certifications, government cloud regions (GovCloud), and HIPAA-eligible services make it the default for regulated industries. Healthcare, financial services, defense — if compliance is your primary constraint, AWS has the most established track record.
- Serverless. Lambda was first, it’s still the most mature, and the ecosystem around it — API Gateway, Step Functions, DynamoDB, EventBridge — is the most complete serverless architecture stack available. Not even close.
Where AWS falls short: The console is a mess. The pricing calculator requires a PhD in cloud economics to use accurately. And the sheer volume of services creates decision paralysis for teams without deep cloud expertise.
Azure: The Enterprise Play
Azure’s strategic advantage has nothing to do with technical superiority. It has everything to do with Microsoft. If your organization runs on Active Directory, Office 365, Windows Server, SQL Server, or Dynamics — Azure integrates with your existing stack in ways that AWS and Google simply can’t match.
Where Azure genuinely excels:
- Microsoft ecosystem integration. Azure AD, hybrid identity, SQL Server licensing mobility, seamless Office 365 integration. If you’re a Microsoft shop, Azure reduces friction dramatically. Your IT team already knows the paradigms.
- Hybrid cloud. Azure Arc and Azure Stack let you extend Azure services into on-premises data centers and other clouds. For organizations that can’t — or won’t — go all-in on public cloud, Azure’s hybrid story is the most coherent of the three.
- Enterprise agreements. Large organizations with existing Microsoft Enterprise Agreements can apply committed spend to Azure. This isn’t a technical advantage, but it’s a financial one that tilts the TCO calculation significantly.
Where Azure falls short: Reliability. I’m going to say it because someone should — Azure has had more high-profile outages than AWS or Google Cloud over the past three years. Their incident communication during outages has been, at times, genuinely poor. The developer experience also lags behind both competitors. Azure’s portal is functional but clunky, and their CLI tooling and SDK quality are inconsistent across services.
Google Cloud: The Engineer’s Platform
Google Cloud is what happens when world-class engineers build a cloud platform and then struggle to sell it. The technology is exceptional. The go-to-market execution has historically been terrible — though it’s improved markedly under Thomas Kurian’s leadership.
Where Google Cloud genuinely excels:
- Data and analytics. BigQuery is, in my professional opinion, the best serverless data warehouse available. Full stop. The ability to query petabytes of data with zero infrastructure management, pay-per-query pricing, and near-instant results is something AWS Redshift and Azure Synapse still can’t fully replicate. If your business is data-intensive, Google Cloud deserves serious consideration.
- Machine learning and AI. Vertex AI, TensorFlow integration, TPUs for training, and pre-trained APIs for vision, language, and translation. Google’s AI and ML capabilities reflect decades of internal ML infrastructure investment. AWS SageMaker is capable, Azure ML is improving, but Google’s ML platform feels native in a way the others don’t.
- Kubernetes. Google invented Kubernetes. GKE (Google Kubernetes Engine) is the best managed Kubernetes offering available — more stable, faster to provision, and better integrated than EKS or AKS. If containers are central to your architecture, GKE alone might justify choosing Google Cloud.
- Network performance. Google’s private global fiber network means traffic between regions is fast. Genuinely fast. Their network tier options let you choose between premium (Google’s backbone) and standard (public internet) routing, which is a level of control the others don’t offer at the same price point.
Where Google Cloud falls short: Enterprise sales support. The partner ecosystem is thinner. The service catalog is narrower — Google Cloud has fewer services than AWS, and some niche capabilities simply don’t exist yet. And Google’s reputation for killing products — earned over years of shuttering consumer services — makes some enterprise buyers nervous about long-term commitment. That concern is overblown for core infrastructure services, but it persists.
The Cost Nobody Puts in the Spreadsheet
Beyond compute, storage, and egress, there are costs that don’t show up in any pricing calculator but absolutely show up in your budget.
Support Tiers
All three vendors charge for support. And the free tiers are essentially useless for production workloads.
- AWS Business Support: Starts at the greater of $100/month or 10% of monthly charges for the first $10K. A company spending $5,000/month on AWS pays $500/month — $6,000/year — just to get phone support with a 1-hour response time for production outages.
- Azure Standard Support: $100/month flat. More predictable, but the response times and quality vary significantly by region.
- Google Cloud Standard Support: $29/month minimum, with Enhanced at $500/month. Their Premium tier runs $12,500/month. Google’s support has historically been the weakest of the three, though they’ve invested heavily in improving it.
These aren’t trivial costs. For a small business, $6,000/year for AWS support is a meaningful line item. Factor it into your comparison.
Engineering Overhead
This is the biggest hidden cost and the hardest to quantify. How much time does your engineering team spend fighting the platform? Learning new services? Debugging platform-specific quirks? Translating between cloud-specific terminology and industry-standard concepts?
AWS’s complexity creates real engineering overhead for small teams. Azure’s inconsistencies create debugging overhead. Google Cloud’s smaller community means fewer answered questions when you’re stuck at 2 AM.
My rough rule of thumb: budget 15-20% of your cloud spend in engineering time for platform management and optimization. That percentage is highest with AWS — not because it’s worse, but because there are more knobs to turn and more ways to accidentally leave them in an expensive position.
Vendor Lock-In: The Uncomfortable Truth
Every cloud provider wants to lock you in. They build proprietary services with unique APIs specifically to make migration expensive. This isn’t cynicism — it’s business strategy, and they’re very good at it.
The lock-in spectrum runs from “easy to leave” to “good luck”:
- Lowest lock-in: Compute VMs, container workloads on Kubernetes, standard databases (PostgreSQL, MySQL). These are relatively portable across all three platforms.
- Medium lock-in: Managed databases with provider-specific features (Aurora on AWS, Cosmos DB on Azure, Spanner on Google Cloud). Cloud-native monitoring, logging, and identity services.
- Highest lock-in: Serverless functions (Lambda, Azure Functions, Cloud Functions), proprietary AI/ML pipelines, data warehouses (Redshift, Synapse, BigQuery), and provider-specific networking constructs.
The honest advice? Accept some lock-in. Trying to stay cloud-agnostic across every layer of your stack is expensive, adds complexity, and means you never get the full benefit of any platform. Use portable technologies where it’s cheap to do so — Kubernetes, Terraform, standard databases — and accept provider-specific services where they deliver genuine value.
Multi-cloud strategies sound great in vendor-neutral consulting decks. In practice, for companies under $50 million in revenue, multi-cloud is usually just multi-headache. You end up needing expertise in multiple platforms, managing multiple billing systems, and maintaining multiple deployment pipelines. Pick one primary provider. Use a second only for specific services where it’s clearly superior — like BigQuery for analytics alongside AWS for everything else.
And whatever you choose, securing your cloud infrastructure from day one is non-negotiable. Security misconfigurations are the number one cause of cloud breaches, and they happen on every platform equally.
Hybrid and Multi-Cloud: When It Actually Makes Sense
Hybrid cloud — running some workloads on-premises and some in the public cloud — is a legitimate architecture pattern for exactly three scenarios:
- Regulatory requirements force certain data to stay on-premises or in specific geographic locations that a cloud provider doesn’t serve.
- Latency-sensitive workloads need to be physically close to on-premises equipment — manufacturing floors, medical devices, point-of-sale systems.
- Legacy systems can’t be migrated yet, and you need cloud connectivity while you plan the transition.
Outside of those scenarios, hybrid is usually just a fancy word for “we haven’t finished migrating.” Azure has the strongest hybrid story with Azure Arc and Azure Stack HCI. AWS has Outposts — essentially an AWS rack in your data center. Google has Anthos, which extends GKE to on-premises and multi-cloud environments.
If hybrid is genuinely your requirement, Azure should be at the top of your shortlist. Its hybrid integration is the most mature and the most natural for organizations with existing on-premises Microsoft infrastructure.
The Decision Framework: Just Tell Me What to Pick
Fine. Here’s my opinionated framework, built from years of watching what actually works.
Choose AWS if:
- You’re in a regulated industry (healthcare, finance, government) and compliance certifications are a hard requirement
- You need the broadest service catalog and are willing to invest in AWS-specific expertise
- Your team already has AWS experience and certifications
- Serverless is central to your architecture
Choose Azure if:
- Your organization is deeply embedded in the Microsoft ecosystem (Active Directory, Office 365, SQL Server, Dynamics)
- You have an existing Microsoft Enterprise Agreement with committed cloud spend
- Hybrid cloud is a genuine architectural requirement, not a buzzword
- Your IT team’s skill set is Windows-centric
Choose Google Cloud if:
- Data analytics, machine learning, or AI capabilities are core to your business — not supplementary, but core
- You’re running containerized workloads and Kubernetes is your orchestration layer
- Cost efficiency on compute is a primary concern and you want automatic discounts without commitment complexity
- You’re a startup or mid-market company that values developer experience and modern tooling over enterprise legacy support
Choose based on workload, not brand, if:
- You’re a small business running standard web applications with no strong platform preference — in which case, Google Cloud will probably save you the most money
- You’re building something new with no legacy constraints — evaluate all three against your specific technical requirements
The Startup Recommendation
If you’re a startup with a small engineering team, limited cloud expertise, and a need to move fast without overspending: Google Cloud. The automatic sustained-use discounts, competitive compute pricing, superior Kubernetes experience, and $300 free trial credit (plus startup program credits that can reach $100,000) make it the best starting point. And if your startup is data-intensive or ML-focused, it isn’t even a close call.
The Enterprise Recommendation
If you’re an enterprise with existing Microsoft licensing, an IT team trained on Windows Server, and compliance requirements that demand extensive audit trails: Azure. The integration advantages alone save enough engineering time to offset any per-unit pricing differences.
The “I Need Everything” Recommendation
If you’re building complex, polyglot infrastructure with demanding requirements across compute, storage, networking, AI, IoT, and serverless — and you have the engineering talent to navigate the complexity: AWS. It has the most services, the most regions, the deepest documentation, and the largest talent pool.
The Bottom Line
The cloud provider decision matters less than most people think and more than the vendors want you to believe. It matters less because all three platforms can run virtually any workload competently. It matters more because the cumulative cost of a poor fit — in dollars, engineering hours, and opportunity cost — compounds over years.
Don’t pick a cloud provider because a conference speaker made it sound exciting. Don’t pick one because your last company used it. Don’t pick one because it’s the “safe” choice. Pick the one that matches your team’s skills, your workload’s requirements, and your budget’s reality. And then commit to it hard enough to actually benefit from what it offers — because the worst possible outcome is spending three years half-committed to a platform and getting none of its advantages.
That’s not a compromise. That’s just waste.
Category: Technology Tags: cloud computing, AWS, Azure, Google Cloud, cloud pricing, cloud comparison, vendor lock-in, cloud migration, IaaS, PaaS Internal Links:
